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source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT 6.262 Discrete Stochastic Processes, Spring 2011
View the complete course: http://ocw.mit.edu/6-262S11
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1. Introduction and Probability Review 1:16:27
2. More Review; The Bernoulli Process 1:08:20
3. Law of Large Numbers, Convergence 1:21:28
4. Poisson (the Perfect Arrival Process) 1:17:14
5. Poisson Combining and Splitting 1:24:32
6. From Poisson to Markov 1:19:17
7. Finite-state Markov Chains; The Matrix Approach 55:34
8. Markov Eigenvalues and Eigenvectors 1:23:38
9. Markov Rewards and Dynamic Programming 1:23:36
10. Renewals and the Strong Law of Large Numbers 1:21:53
11. Renewals: Strong Law and Rewards 1:18:17
12. Renewal Rewards, Stopping Trials, and Wald's Inequality 1:26:21
13. Little, M/G/1, Ensemble Averages 1:14:53
14. Review 1:19:19
15. The Last Renewal 1:15:44
16. Renewals and Countable-state Markov 1:19:40
17. Countable-state Markov Chains 1:23:46
18. Countable-state Markov Chains and Processes 1:16:29
19. Countable-state Markov Processes 1:22:14
20. Markov Processes and Random Walks 1:23:09
21. Hypothesis Testing and Random Walks 1:25:23
22. Random Walks and Thresholds 1:21:17
23. Martingales (Plain, Sub, and Super) 1:22:40
24. Martingales: Stopping and Converging 1:20:44
25. Putting It All Together 1:21:27
